The present invention relates to the field of information retrieval systems, and more particularly to dialogue services that enable user interaction with information retrieval systems.
Information retrieval systems are typically implemented to provide information in response to a request. An example of an information retrieval system is a Question-Answering (QA) system. QA systems retrieve or construct answers to queries using a collection of documents or information (such as the World Wide Web).
It is fairly typical for queries to be posed in natural language, and so complex Natural Language Processing (NLP) techniques can be needed in order to correctly handle such queries.
Dialogue Services (such as virtual agents) have been devised to understand, diagnose and solve queries, and so they are typically implemented in conjunction with QA systems (and other forms of information retrieval systems). Such dialogue services may be tailored to particular (knowledge) domains or instances of an information retrieval system in an attempt to improve handling (e.g. improve understanding, diagnosis, and/or solving) of queries.
Where a particular version of an information retrieval system is created for a particular customer or user, that version is typically referred to as an “instance.” An instance of an information retrieval system may therefore comprise an associated dialogue service (which may be specifically tailored to the instance).
For each instance or dialogue service within a knowledge domain, some of the corpus/language knowledge may be considered sensitive or protected, although common terms may be shared with other instances.
For example, a customer/user for an instance created for Company A might always refer to personal insurance as “Plan X.” Thus, if the customer/user talks to (e.g. queries) another instance which has been created for a different company (Company B, for example) mentioning Plan X may raise some issues. By way of example, the instance for Company B may have no context for Plan X, or an incorrect context. The instance for Company B may therefore provide an incorrect answer (or no answer at all).
In addition, Company B may be able to determine that the customer/user is using a context that is associated with a competitor (due to using terms related to Company A for example). Company A may not wish for this to happen. Furthermore, the customer/user may also be unaware that they are indicating such information.
Further, even if the context is not personal, Company B would need to have their dialogue service adapted to understand the context, and Company B may not be able to keep up with a competitor's terms as they change, nor would they normally want to.
Also, Plan X may, for example, be an internal plan that should not be divulged outside of that domain. The user may for instance be an employee for Company A and not realize they should not discuss it.
Such exemplary issues become further compounded when a dialogue service is integrated to a cognitive system, as the leaking of information may become easier because, for example, a person may talk to the instance to get it to respond in terms that may be confidential, sensitive, or imply additional information.
Accordingly, a problem faced is how to protect domain-specific language in information systems that use dialogue services.